Abstract

In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation 90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.

title = "Evaluation and intercomparison of downscaled daily precipitation indices over Japan in present-day climate: Strengths and weaknesses of dynamical and bias correction-type statistical downscaling methods",

abstract = "In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation 90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.",

N2 - In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation 90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.

AB - In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation 1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation 90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation 90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.